Standard compliant data projects

A tale about applying standards to data projects and if it’s necessary.

Photo by Philipp Mandler on Unsplash


By Kenneth Jensen - Own work based on: (Figure 1), CC BY-SA 3.0,
By Kenneth Jensen — Own work based on: (Figure 1), CC BY-SA 3.0,
  1. Business Understanding
    At the beginning you define goals and requirements. What do you want to achieve with your project?
  2. Data Understanding
    Collect and understand existing data. At this phase you could identify problems with your data or with quality of data.
  3. Data Preparation
    Self-explaining phase: Prepare and clean your existing data for your desired models and goals of your projects.
  4. Modelling
    Create your models and optimizing parameters. Usually in this step, more than one model is being created.
  5. Evaluation
    In this step you evaluate which model may fit best for your current goal and requirements. It is necessary to check with your initial goal to be sure to match requirements.
  6. Deployment
    In this final step, you „deploy“ your results. Could mean you have a presentation or a deliverable system using your model. It depends on your goals.

IBM revised the process

Analytics Solutions Unified Method (ASUM) Process Model.
  1. Analyze
    As in CRISP-DM, you define your goals and requirements first.
  2. Design
    Defining components, development environments and needed Resources to complete the task
  3. Configure & Build
    The needed components are gradually implemented and tested. At his step you develop models and test them.
  4. Deploy
    Integrate the developed components in your final environment.
  5. Operate and Optimize
    Continuous optimization is important which could lead into new requirements.

IBM DataFirst Method

IBM DataFirst Method Process Model. Source: IBM Corporation

Which method should I use?



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Carsten Sandtner

Tech, Travel, and Life. Apple addict who loves travelling with his camper van and writing about mentioned topics.